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[ARTICLE · art-170050] src=nathanclonts.com ↗ pub= topic=neural-networks verified=true sentiment=↑ positive

Visualizing connections in neural networks across various modalities

A developer published an open-source tool at github.com/naclonts/neural-net-visual that renders the connection weights of open model weights as 2D images, with the X axis showing computational stages and the Y axis showing neuron index. The visualizations cover seven models: ResNet20 on CIFAR-10, an MNIST MLP, MobileNetV2 (1,000 categories), TinyStories 1M and 8M, EnCodec at 24 kHz, and MusicGen Small. The rendering relies on Datashader's network bundling code and was inspired by Julian Rogawski's nn_vis library, which generates 3D models of neural networks.

read1 min views1 publishedNov 15, 2026

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ResNet20 · CIFAR-10 · Image classification into 10 categories

MNIST MLP · Image classification into 10 digits

MobileNetV2 — Image classification into 1,000 categories

TinyStories 1M · Text to Text

TinyStories 8M · Text to Text

EnCodec · 24 kHz · Audio to Audio

MusicGen Small · Text to Audio

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These images are generated from open model weights. The X axis represents computational stages, including layers and other computational operations like residual additions. The Y axis represents the index of the neuron in each stage. The lines are bundles of connections between neurons in each stage (grouped together for clarity), with brightness based on the total absolute weights of the connections represented by the line.

I made this for fun, curiosity, and to appreciate the aesthetic beauty of these marvellous machines. While these representations are highly simplified (displaying weights with hundreds of dimensions onto a 2-dimensional plane, for one), I like to think of them as fingerprints of the models that, combined with much other study, could help us intuit neural networks' structures.

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The code to generate these is at https://github.com/naclonts/neural-net-visual/. Try pulling other models and riffing on it!

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I was inspired to play around with this by Julian Rogawski's nn_vis library, which generates 3D models representing neural networks.

The rendering relies on Datashader's network bundling code. (Datashader is a sick library btw - check out the homepage.)

Model cards on HuggingFace:

- MNIST MLP: [dacorvo/mnist-mlp](https://huggingface.co/dacorvo/mnist-mlp)
- ResNet20 · CIFAR-10: [sapbot/resnet20-cifar10-272k](https://huggingface.co/sapbot/resnet20-cifar10-272k)
- MobileNetV2: [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224)
- TinyStories 1M: [roneneldan/TinyStories-1M](https://huggingface.co/roneneldan/TinyStories-1M)
- TinyStories 8M: [roneneldan/TinyStories-8M](https://huggingface.co/roneneldan/TinyStories-8M)
- EnCodec · 24 kHz: [facebook/encodec_24khz](https://huggingface.co/facebook/encodec_24khz)
- MusicGen Small: [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small)

Another fun view of MusicGen small.

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